The use of embedded models is an approach different from the traditional error corrections to trajectory and ideal formation structure of a matrix to morph to different structures to maximize throughput along the trajectory. Other well known approaches include:

1. Task Assignment Algorithms These determine which drone goes to which position in the next formation.

a. Hungarian Algorithm: Often used to minimize total travel distance during formation transitions.

b. Dynamic Task Assignment: Adjusts assignments in real time to account for deviations or potential collisions.

2. Trajectory Planning Algorithms These generate smooth, collision-free paths for each drone.

a. Dubins Path Planning: Ensures drones follow feasible curved paths, especially for fixed-wing drones.

b. Bezier or B-spline Curves: Used for smooth interpolation between waypoints.

c. Artificial Potential Fields: Create repulsive forces to avoid collisions while guiding drones to targets.

3. Formation Transformation Models

a. Six-Tuple State Coherence (STSC): Ensures all drones maintain consistent position, heading, and speed during transitions.

b. DFCA (Drone Formation Change Algorithm): Combines centralized assignment with distributed path planning to maintain formation integrity.

4. Swarm Coordination and Consensus Algorithms

a. Consensus-Based Control: Ensures all drones agree on shared parameters like velocity and heading.

b. Flocking Algorithms: Inspired by bird flocks, these maintain cohesion and spacing dynamically.

5. Optimization Techniques

a. Particle Swarm Optimization (PSO) and Genetic Algorithms: Sometimes used to optimize formation layouts or transitions under constraints.

6. Simulation and Animation Tools Commercial systems often include 3D animation interfaces to design and preview formations before deployment, ensuring feasibility and visual appeal.